Interpretable Machine Learning Using Partial Linear Models*

Emmanuel Flachaire, Sullivan Hué, Sébastien Laurent () and Gilles Hacheme
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Emmanuel Flachaire: AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, AMU - Aix Marseille Université, CNRS - Centre National de la Recherche Scientifique
Sébastien Laurent: AMU - Aix Marseille Université, CNRS - Centre National de la Recherche Scientifique, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, Aix-Marseille Graduate School of Management
Gilles Hacheme: AMU - Aix Marseille Université, CNRS - Centre National de la Recherche Scientifique, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique

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Abstract: Despite their high predictive performance, random forest and gradient boosting are often considered as black boxes which has raised concerns from practitioners and regulators. As an alternative, we suggest using partial linear models that are inherently interpretable. Specifically, we propose to combine parametric and non‐parametric functions to accurately capture linearities and non‐linearities prevailing between dependent and explanatory variables, and a variable selection procedure to control for overfitting issues. Estimation relies on a two‐step procedure building upon the double residual method. We illustrate the predictive performance and interpretability of our approach on a regression problem.

Keywords: Machine leaning; Lasso; Autometrics; GAM (search for similar items in EconPapers)
Date: 2023-12-28
Note: View the original document on HAL open archive server: https://hal.science/hal-04529011v1
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Published in Oxford Bulletin of Economics and Statistics, 2023, ⟨10.1111/obes.12592⟩

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Journal Article: Interpretable Machine Learning Using Partial Linear Models (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04529011

DOI: 10.1111/obes.12592

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